Publications by authors named "I Kamer"

Over the last two decades, the diagnosis and treatment of breast cancer patients have considerably improved. However, brain metastases remain a major clinical challenge and a leading cause of mortality. Thus, a better understanding of the pathways involved in the metastatic cascade is essential.

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Cancer exploits different mechanisms to escape T-cell immunosurveillance, including overexpression of checkpoint ligands, secretion of immunosuppressive molecules, and aberrant glycosylation. Herein, we report that IFNγ, a potent immunomodulator secreted in the tumor microenvironment, can induce α2,6 hypersialylation in cancer cell lines derived from various histologies. We focused on Siglec-9, a receptor for sialic acid moieties, and demonstrated that the Siglec-9+ T-cell population displayed reduced effector function.

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Purpose: Current guidelines for the management of metastatic non-small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions.

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Article Synopsis
  • Current anti-cancer immunotherapies, while successful, only work for a small group of patients, highlighting the need for better biomarkers to predict treatment responses.
  • Researchers discovered that interferon-stimulated, Ly6E neutrophils can serve as a predictive biomarker for anti-PD1 therapy in mice, showing how they can enhance tumor response by activating T cells.
  • The study further confirmed that Ly6E neutrophils are an effective predictor of immunotherapy responses in humans with lung cancer and melanoma, achieving high accuracy in patient cohorts and public data analysis.
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Lung cancer is the leading cause of cancer-related mortality worldwide. Early detection is essential to achieving a better outcome and prognosis. Volatile organic compounds (VOCs) reflect alterations in the pathophysiology and body metabolism processes, as shown in various types of cancers.

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